Overview

Dataset statistics

Number of variables31
Number of observations2618
Missing cells14276
Missing cells (%)17.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.2 KiB
Average record size in memory248.0 B

Variable types

Categorical9
Numeric22

Alerts

Posiadanie piłki H has a high cardinality: 53 distinct valuesHigh cardinality
Posiadanie piłki A has a high cardinality: 53 distinct valuesHigh cardinality
Sędzia has a high cardinality: 55 distinct valuesHigh cardinality
Stadion has a high cardinality: 65 distinct valuesHigh cardinality
Sytuacje bramkowe H is highly overall correlated with Strzały na bramkę H and 3 other fieldsHigh correlation
Sytuacje bramkowe A is highly overall correlated with Strzały na bramkę A and 3 other fieldsHigh correlation
Strzały na bramkę H is highly overall correlated with Sytuacje bramkowe H and 1 other fieldsHigh correlation
Strzały na bramkę A is highly overall correlated with Sytuacje bramkowe A and 1 other fieldsHigh correlation
Strzały niecelne H is highly overall correlated with Sytuacje bramkowe HHigh correlation
Strzały niecelne A is highly overall correlated with Sytuacje bramkowe AHigh correlation
Strzały zablokowane H is highly overall correlated with Sytuacje bramkowe HHigh correlation
Strzały zablokowane A is highly overall correlated with Sytuacje bramkowe AHigh correlation
Rzuty wolne H is highly overall correlated with Faule AHigh correlation
Rzuty wolne A is highly overall correlated with Faule HHigh correlation
Interwencje bramkarzy H is highly overall correlated with Sytuacje bramkowe A and 1 other fieldsHigh correlation
Interwencje bramkarzy A is highly overall correlated with Sytuacje bramkowe H and 1 other fieldsHigh correlation
Faule H is highly overall correlated with Rzuty wolne AHigh correlation
Faule A is highly overall correlated with Rzuty wolne HHigh correlation
Gospodarze is highly overall correlated with StadionHigh correlation
Posiadanie piłki H is highly overall correlated with Posiadanie piłki AHigh correlation
Posiadanie piłki A is highly overall correlated with Posiadanie piłki HHigh correlation
Czerwone kartki H is highly overall correlated with Czerwone kartki AHigh correlation
Czerwone kartki A is highly overall correlated with Czerwone kartki HHigh correlation
Stadion is highly overall correlated with GospodarzeHigh correlation
Strzały zablokowane H has 1621 (61.9%) missing valuesMissing
Strzały zablokowane A has 1621 (61.9%) missing valuesMissing
Rzuty wolne H has 1569 (59.9%) missing valuesMissing
Rzuty wolne A has 1569 (59.9%) missing valuesMissing
Wrzuty z autu H has 1570 (60.0%) missing valuesMissing
Wrzuty z autu A has 1570 (60.0%) missing valuesMissing
Czerwone kartki H has 2169 (82.8%) missing valuesMissing
Czerwone kartki A has 2169 (82.8%) missing valuesMissing
Żółte kartki H has 65 (2.5%) missing valuesMissing
Żółte kartki A has 65 (2.5%) missing valuesMissing
Sędzia has 46 (1.8%) missing valuesMissing
Stadion has 188 (7.2%) missing valuesMissing
Strzały na bramkę H has 30 (1.1%) zerosZeros
Strzały na bramkę A has 59 (2.3%) zerosZeros
Strzały zablokowane H has 77 (2.9%) zerosZeros
Strzały zablokowane A has 79 (3.0%) zerosZeros
Rzuty rożne H has 33 (1.3%) zerosZeros
Rzuty rożne A has 49 (1.9%) zerosZeros
Spalone H has 519 (19.8%) zerosZeros
Spalone A has 574 (21.9%) zerosZeros
Interwencje bramkarzy H has 172 (6.6%) zerosZeros
Interwencje bramkarzy A has 108 (4.1%) zerosZeros
Żółte kartki H has 272 (10.4%) zerosZeros
Żółte kartki A has 201 (7.7%) zerosZeros

Reproduction

Analysis started2023-05-09 12:05:15.567251
Analysis finished2023-05-09 12:06:12.223647
Duration56.66 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Gospodarze
Categorical

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Legia Warszawa
 
165
Lech Poznań
 
163
Jagiellonia Białystok
 
163
Śląsk Wrocław
 
163
Cracovia
 
162
Other values (24)
1802 

Length

Max length21
Median length18
Mean length13.166539
Min length8

Characters and Unicode

Total characters34470
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGórnik Zabrze
2nd rowLegia Warszawa
3rd rowRuch Chorzów
4th rowWisła Kraków
5th rowCracovia

Common Values

ValueCountFrequency (%)
Legia Warszawa 165
 
6.3%
Lech Poznań 163
 
6.2%
Jagiellonia Białystok 163
 
6.2%
Śląsk Wrocław 163
 
6.2%
Cracovia 162
 
6.2%
Piast Gliwice 162
 
6.2%
Pogoń Szczecin 161
 
6.1%
Wisła Kraków 160
 
6.1%
Lechia Gdańsk 160
 
6.1%
Zagłębie Lubin 143
 
5.5%
Other values (19) 1016
38.8%

Length

2023-05-09T14:06:12.293166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wisła 267
 
5.2%
górnik 213
 
4.2%
poznań 195
 
3.8%
legia 165
 
3.2%
warszawa 165
 
3.2%
białystok 163
 
3.2%
lech 163
 
3.2%
jagiellonia 163
 
3.2%
wrocław 163
 
3.2%
śląsk 163
 
3.2%
Other values (43) 3272
64.3%

Most occurring characters

ValueCountFrequency (%)
a 3786
 
11.0%
i 2912
 
8.4%
2474
 
7.2%
e 1924
 
5.6%
o 1905
 
5.5%
c 1723
 
5.0%
r 1385
 
4.0%
s 1292
 
3.7%
k 1250
 
3.6%
z 1234
 
3.6%
Other values (38) 14585
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26477
76.8%
Uppercase Letter 5306
 
15.4%
Space Separator 2474
 
7.2%
Dash Punctuation 142
 
0.4%
Other Punctuation 71
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3786
14.3%
i 2912
 
11.0%
e 1924
 
7.3%
o 1905
 
7.2%
c 1723
 
6.5%
r 1385
 
5.2%
s 1292
 
4.9%
k 1250
 
4.7%
z 1234
 
4.7%
n 1205
 
4.6%
Other values (18) 7861
29.7%
Uppercase Letter
ValueCountFrequency (%)
P 696
13.1%
L 650
12.3%
W 645
12.2%
G 627
11.8%
B 501
9.4%
K 456
8.6%
Z 339
6.4%
C 286
5.4%
S 283
5.3%
Ś 163
 
3.1%
Other values (7) 660
12.4%
Space Separator
ValueCountFrequency (%)
2474
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%
Other Punctuation
ValueCountFrequency (%)
. 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31783
92.2%
Common 2687
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3786
 
11.9%
i 2912
 
9.2%
e 1924
 
6.1%
o 1905
 
6.0%
c 1723
 
5.4%
r 1385
 
4.4%
s 1292
 
4.1%
k 1250
 
3.9%
z 1234
 
3.9%
n 1205
 
3.8%
Other values (35) 13167
41.4%
Common
ValueCountFrequency (%)
2474
92.1%
- 142
 
5.3%
. 71
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31717
92.0%
None 2753
 
8.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3786
 
11.9%
i 2912
 
9.2%
2474
 
7.8%
e 1924
 
6.1%
o 1905
 
6.0%
c 1723
 
5.4%
r 1385
 
4.4%
s 1292
 
4.1%
k 1250
 
3.9%
z 1234
 
3.9%
Other values (30) 11832
37.3%
None
ValueCountFrequency (%)
ł 879
31.9%
ó 551
20.0%
ń 516
18.7%
ę 283
 
10.3%
ą 181
 
6.6%
Ś 163
 
5.9%
Ł 125
 
4.5%
ź 55
 
2.0%

Goście
Categorical

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
Lechia Gdańsk
 
163
Wisła Kraków
 
163
Pogoń Szczecin
 
162
Cracovia
 
161
Piast Gliwice
 
161
Other values (24)
1808 

Length

Max length21
Median length18
Mean length13.158136
Min length8

Characters and Unicode

Total characters34448
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLechia Gdańsk
2nd rowLech Poznań
3rd rowPogoń Szczecin
4th rowZawisza Bydgoszcz
5th rowJagiellonia Białystok

Common Values

ValueCountFrequency (%)
Lechia Gdańsk 163
 
6.2%
Wisła Kraków 163
 
6.2%
Pogoń Szczecin 162
 
6.2%
Cracovia 161
 
6.1%
Piast Gliwice 161
 
6.1%
Jagiellonia Białystok 160
 
6.1%
Śląsk Wrocław 160
 
6.1%
Lech Poznań 160
 
6.1%
Legia Warszawa 158
 
6.0%
Górnik Zabrze 144
 
5.5%
Other values (19) 1026
39.2%

Length

2023-05-09T14:06:12.396225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wisła 268
 
5.3%
górnik 218
 
4.3%
poznań 192
 
3.8%
gdańsk 163
 
3.2%
lechia 163
 
3.2%
kraków 163
 
3.2%
szczecin 162
 
3.2%
pogoń 162
 
3.2%
zagłębie 162
 
3.2%
piast 161
 
3.2%
Other values (43) 3280
64.4%

Most occurring characters

ValueCountFrequency (%)
a 3759
 
10.9%
i 2902
 
8.4%
2476
 
7.2%
e 1918
 
5.6%
o 1894
 
5.5%
c 1726
 
5.0%
r 1388
 
4.0%
s 1285
 
3.7%
k 1254
 
3.6%
z 1239
 
3.6%
Other values (38) 14607
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26440
76.8%
Uppercase Letter 5314
 
15.4%
Space Separator 2476
 
7.2%
Dash Punctuation 144
 
0.4%
Other Punctuation 74
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3759
14.2%
i 2902
 
11.0%
e 1918
 
7.3%
o 1894
 
7.2%
c 1726
 
6.5%
r 1388
 
5.2%
s 1285
 
4.9%
k 1254
 
4.7%
z 1239
 
4.7%
n 1208
 
4.6%
Other values (18) 7867
29.8%
Uppercase Letter
ValueCountFrequency (%)
P 690
13.0%
L 642
12.1%
W 637
12.0%
G 635
11.9%
B 505
9.5%
K 459
8.6%
Z 344
6.5%
S 289
5.4%
C 286
5.4%
Ś 160
 
3.0%
Other values (7) 667
12.6%
Space Separator
ValueCountFrequency (%)
2476
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%
Other Punctuation
ValueCountFrequency (%)
. 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31754
92.2%
Common 2694
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3759
 
11.8%
i 2902
 
9.1%
e 1918
 
6.0%
o 1894
 
6.0%
c 1726
 
5.4%
r 1388
 
4.4%
s 1285
 
4.0%
k 1254
 
3.9%
z 1239
 
3.9%
n 1208
 
3.8%
Other values (35) 13181
41.5%
Common
ValueCountFrequency (%)
2476
91.9%
- 144
 
5.3%
. 74
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31682
92.0%
None 2766
 
8.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3759
 
11.9%
i 2902
 
9.2%
2476
 
7.8%
e 1918
 
6.1%
o 1894
 
6.0%
c 1726
 
5.4%
r 1388
 
4.4%
s 1285
 
4.1%
k 1254
 
4.0%
z 1239
 
3.9%
Other values (30) 11841
37.4%
None
ValueCountFrequency (%)
ł 874
31.6%
ó 563
20.4%
ń 517
18.7%
ę 286
 
10.3%
ą 179
 
6.5%
Ś 160
 
5.8%
Ł 131
 
4.7%
ź 56
 
2.0%

Posiadanie piłki H
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct53
Distinct (%)2.0%
Missing2
Missing (%)0.1%
Memory size20.6 KiB
55%
 
142
56%
 
140
54%
 
129
53%
 
128
52%
 
125
Other values (48)
1952 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7848
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row52%
2nd row49%
3rd row45%
4th row49%
5th row63%

Common Values

ValueCountFrequency (%)
55% 142
 
5.4%
56% 140
 
5.3%
54% 129
 
4.9%
53% 128
 
4.9%
52% 125
 
4.8%
58% 123
 
4.7%
51% 121
 
4.6%
49% 117
 
4.5%
46% 116
 
4.4%
50% 107
 
4.1%
Other values (43) 1368
52.3%

Length

2023-05-09T14:06:12.496783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
55 142
 
5.4%
56 140
 
5.4%
54 129
 
4.9%
53 128
 
4.9%
52 125
 
4.8%
58 123
 
4.7%
51 121
 
4.6%
49 117
 
4.5%
46 116
 
4.4%
50 107
 
4.1%
Other values (43) 1368
52.3%

Most occurring characters

ValueCountFrequency (%)
% 2616
33.3%
5 1484
18.9%
4 1128
14.4%
6 664
 
8.5%
3 430
 
5.5%
2 272
 
3.5%
7 270
 
3.4%
8 267
 
3.4%
9 248
 
3.2%
1 239
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5232
66.7%
Other Punctuation 2616
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1484
28.4%
4 1128
21.6%
6 664
12.7%
3 430
 
8.2%
2 272
 
5.2%
7 270
 
5.2%
8 267
 
5.1%
9 248
 
4.7%
1 239
 
4.6%
0 230
 
4.4%
Other Punctuation
ValueCountFrequency (%)
% 2616
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 2616
33.3%
5 1484
18.9%
4 1128
14.4%
6 664
 
8.5%
3 430
 
5.5%
2 272
 
3.5%
7 270
 
3.4%
8 267
 
3.4%
9 248
 
3.2%
1 239
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 2616
33.3%
5 1484
18.9%
4 1128
14.4%
6 664
 
8.5%
3 430
 
5.5%
2 272
 
3.5%
7 270
 
3.4%
8 267
 
3.4%
9 248
 
3.2%
1 239
 
3.0%

Posiadanie piłki A
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct53
Distinct (%)2.0%
Missing2
Missing (%)0.1%
Memory size20.6 KiB
45%
 
142
44%
 
140
46%
 
129
47%
 
128
48%
 
125
Other values (48)
1952 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7848
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row48%
2nd row51%
3rd row55%
4th row51%
5th row37%

Common Values

ValueCountFrequency (%)
45% 142
 
5.4%
44% 140
 
5.3%
46% 129
 
4.9%
47% 128
 
4.9%
48% 125
 
4.8%
42% 123
 
4.7%
49% 121
 
4.6%
51% 117
 
4.5%
54% 116
 
4.4%
50% 107
 
4.1%
Other values (43) 1368
52.3%

Length

2023-05-09T14:06:12.587447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45 142
 
5.4%
44 140
 
5.4%
46 129
 
4.9%
47 128
 
4.9%
48 125
 
4.8%
42 123
 
4.7%
49 121
 
4.6%
51 117
 
4.5%
54 116
 
4.4%
50 107
 
4.1%
Other values (43) 1368
52.3%

Most occurring characters

ValueCountFrequency (%)
% 2616
33.3%
4 1470
18.7%
5 1211
15.4%
3 545
 
6.9%
6 473
 
6.0%
2 279
 
3.6%
7 274
 
3.5%
8 263
 
3.4%
1 248
 
3.2%
9 239
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5232
66.7%
Other Punctuation 2616
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1470
28.1%
5 1211
23.1%
3 545
 
10.4%
6 473
 
9.0%
2 279
 
5.3%
7 274
 
5.2%
8 263
 
5.0%
1 248
 
4.7%
9 239
 
4.6%
0 230
 
4.4%
Other Punctuation
ValueCountFrequency (%)
% 2616
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 2616
33.3%
4 1470
18.7%
5 1211
15.4%
3 545
 
6.9%
6 473
 
6.0%
2 279
 
3.6%
7 274
 
3.5%
8 263
 
3.4%
1 248
 
3.2%
9 239
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 2616
33.3%
4 1470
18.7%
5 1211
15.4%
3 545
 
6.9%
6 473
 
6.0%
2 279
 
3.6%
7 274
 
3.5%
8 263
 
3.4%
1 248
 
3.2%
9 239
 
3.0%

Sytuacje bramkowe H
Real number (ℝ)

Distinct34
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.523118
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:12.686106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q110
median13
Q317
95-th percentile22
Maximum42
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8328661
Coefficient of variation (CV)0.35737809
Kurtosis0.85260391
Mean13.523118
Median Absolute Deviation (MAD)3
Skewness0.57713585
Sum35390
Variance23.356595
MonotonicityNot monotonic
2023-05-09T14:06:12.925702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
14 228
 
8.7%
11 225
 
8.6%
12 207
 
7.9%
13 204
 
7.8%
15 200
 
7.6%
10 185
 
7.1%
16 170
 
6.5%
9 161
 
6.1%
17 153
 
5.8%
18 125
 
4.8%
Other values (24) 759
29.0%
ValueCountFrequency (%)
2 4
 
0.2%
3 10
 
0.4%
4 15
 
0.6%
5 42
 
1.6%
6 80
 
3.1%
7 108
4.1%
8 121
4.6%
9 161
6.1%
10 185
7.1%
11 225
8.6%
ValueCountFrequency (%)
42 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 3
 
0.1%
30 2
 
0.1%
29 8
0.3%
28 5
 
0.2%
27 11
0.4%
26 13
0.5%

Sytuacje bramkowe A
Real number (ℝ)

Distinct29
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.598013
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.037416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median11
Q314
95-th percentile20
Maximum30
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.441994
Coefficient of variation (CV)0.38299613
Kurtosis0.32890538
Mean11.598013
Median Absolute Deviation (MAD)3
Skewness0.55288511
Sum30352
Variance19.731311
MonotonicityNot monotonic
2023-05-09T14:06:13.134787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
9 241
 
9.2%
10 241
 
9.2%
11 237
 
9.1%
12 225
 
8.6%
8 216
 
8.3%
13 197
 
7.5%
14 166
 
6.3%
7 156
 
6.0%
15 154
 
5.9%
6 124
 
4.7%
Other values (19) 660
25.2%
ValueCountFrequency (%)
1 3
 
0.1%
2 6
 
0.2%
3 21
 
0.8%
4 55
 
2.1%
5 92
 
3.5%
6 124
4.7%
7 156
6.0%
8 216
8.3%
9 241
9.2%
10 241
9.2%
ValueCountFrequency (%)
30 3
 
0.1%
29 2
 
0.1%
27 1
 
< 0.1%
26 4
 
0.2%
25 7
 
0.3%
24 9
 
0.3%
23 14
 
0.5%
22 19
 
0.7%
21 31
1.2%
20 53
2.0%

Strzały na bramkę H
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9289534
Minimum0
Maximum16
Zeros30
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.231571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q36
95-th percentile9
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4678897
Coefficient of variation (CV)0.50069244
Kurtosis0.33014707
Mean4.9289534
Median Absolute Deviation (MAD)2
Skewness0.57738359
Sum12904
Variance6.0904797
MonotonicityNot monotonic
2023-05-09T14:06:13.322375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 421
16.1%
4 409
15.6%
3 396
15.1%
6 324
12.4%
2 268
10.2%
7 261
10.0%
8 166
 
6.3%
1 122
 
4.7%
9 97
 
3.7%
10 70
 
2.7%
Other values (7) 84
 
3.2%
ValueCountFrequency (%)
0 30
 
1.1%
1 122
 
4.7%
2 268
10.2%
3 396
15.1%
4 409
15.6%
5 421
16.1%
6 324
12.4%
7 261
10.0%
8 166
 
6.3%
9 97
 
3.7%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 2
 
0.1%
14 2
 
0.1%
13 9
 
0.3%
12 14
 
0.5%
11 26
 
1.0%
10 70
 
2.7%
9 97
 
3.7%
8 166
6.3%
7 261
10.0%

Strzały na bramkę A
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1894576
Minimum0
Maximum14
Zeros59
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.418905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2650194
Coefficient of variation (CV)0.54064741
Kurtosis0.35329324
Mean4.1894576
Median Absolute Deviation (MAD)1
Skewness0.61227551
Sum10968
Variance5.130313
MonotonicityNot monotonic
2023-05-09T14:06:13.502048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 470
18.0%
4 455
17.4%
5 385
14.7%
2 365
13.9%
6 272
10.4%
1 209
8.0%
7 191
7.3%
8 92
 
3.5%
9 71
 
2.7%
0 59
 
2.3%
Other values (5) 49
 
1.9%
ValueCountFrequency (%)
0 59
 
2.3%
1 209
8.0%
2 365
13.9%
3 470
18.0%
4 455
17.4%
5 385
14.7%
6 272
10.4%
7 191
7.3%
8 92
 
3.5%
9 71
 
2.7%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
0.1%
12 9
 
0.3%
11 12
 
0.5%
10 25
 
1.0%
9 71
 
2.7%
8 92
 
3.5%
7 191
7.3%
6 272
10.4%
5 385
14.7%

Strzały niecelne H
Real number (ℝ)

Distinct27
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4159664
Minimum0
Maximum28
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.605068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q39
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.652984
Coefficient of variation (CV)0.49258368
Kurtosis1.474551
Mean7.4159664
Median Absolute Deviation (MAD)2
Skewness0.92405651
Sum19415
Variance13.344292
MonotonicityNot monotonic
2023-05-09T14:06:13.696519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7 306
11.7%
5 304
11.6%
6 296
11.3%
8 270
10.3%
4 254
9.7%
9 241
9.2%
3 176
6.7%
10 154
5.9%
11 130
 
5.0%
12 111
 
4.2%
Other values (17) 376
14.4%
ValueCountFrequency (%)
0 4
 
0.2%
1 30
 
1.1%
2 108
 
4.1%
3 176
6.7%
4 254
9.7%
5 304
11.6%
6 296
11.3%
7 306
11.7%
8 270
10.3%
9 241
9.2%
ValueCountFrequency (%)
28 1
 
< 0.1%
26 1
 
< 0.1%
24 2
 
0.1%
23 1
 
< 0.1%
22 2
 
0.1%
21 4
 
0.2%
20 6
 
0.2%
19 10
0.4%
18 9
0.3%
17 20
0.8%

Strzały niecelne A
Real number (ℝ)

Distinct22
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3109244
Minimum0
Maximum22
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.798426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2716115
Coefficient of variation (CV)0.51840449
Kurtosis1.0814311
Mean6.3109244
Median Absolute Deviation (MAD)2
Skewness0.81837754
Sum16522
Variance10.703442
MonotonicityNot monotonic
2023-05-09T14:06:13.889443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5 359
13.7%
6 333
12.7%
4 314
12.0%
7 278
10.6%
3 246
9.4%
8 231
8.8%
9 190
7.3%
2 166
6.3%
10 116
 
4.4%
11 103
 
3.9%
Other values (12) 282
10.8%
ValueCountFrequency (%)
0 16
 
0.6%
1 82
 
3.1%
2 166
6.3%
3 246
9.4%
4 314
12.0%
5 359
13.7%
6 333
12.7%
7 278
10.6%
8 231
8.8%
9 190
7.3%
ValueCountFrequency (%)
22 2
 
0.1%
21 2
 
0.1%
20 4
 
0.2%
18 3
 
0.1%
17 5
 
0.2%
16 15
 
0.6%
15 18
 
0.7%
14 26
 
1.0%
13 37
1.4%
12 72
2.8%

Strzały zablokowane H
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)1.4%
Missing1621
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean3.0972919
Minimum0
Maximum16
Zeros77
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:13.987617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.080113
Coefficient of variation (CV)0.67159089
Kurtosis2.2624406
Mean3.0972919
Median Absolute Deviation (MAD)1
Skewness0.99014472
Sum3088
Variance4.3268702
MonotonicityNot monotonic
2023-05-09T14:06:14.075272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 215
 
8.2%
3 187
 
7.1%
1 150
 
5.7%
4 139
 
5.3%
5 99
 
3.8%
0 77
 
2.9%
6 71
 
2.7%
7 36
 
1.4%
8 11
 
0.4%
10 4
 
0.2%
Other values (4) 8
 
0.3%
(Missing) 1621
61.9%
ValueCountFrequency (%)
0 77
 
2.9%
1 150
5.7%
2 215
8.2%
3 187
7.1%
4 139
5.3%
5 99
3.8%
6 71
 
2.7%
7 36
 
1.4%
8 11
 
0.4%
9 3
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
14 1
 
< 0.1%
11 3
 
0.1%
10 4
 
0.2%
9 3
 
0.1%
8 11
 
0.4%
7 36
 
1.4%
6 71
2.7%
5 99
3.8%
4 139
5.3%

Strzały zablokowane A
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)1.2%
Missing1621
Missing (%)61.9%
Infinite0
Infinite (%)0.0%
Mean2.884654
Minimum0
Maximum11
Zeros79
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:14.171988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9827912
Coefficient of variation (CV)0.68735843
Kurtosis0.45564266
Mean2.884654
Median Absolute Deviation (MAD)1
Skewness0.82151578
Sum2876
Variance3.931461
MonotonicityNot monotonic
2023-05-09T14:06:14.254307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 233
 
8.9%
1 184
 
7.0%
3 182
 
7.0%
4 121
 
4.6%
5 88
 
3.4%
0 79
 
3.0%
6 54
 
2.1%
7 29
 
1.1%
8 17
 
0.6%
9 8
 
0.3%
Other values (2) 2
 
0.1%
(Missing) 1621
61.9%
ValueCountFrequency (%)
0 79
 
3.0%
1 184
7.0%
2 233
8.9%
3 182
7.0%
4 121
4.6%
5 88
 
3.4%
6 54
 
2.1%
7 29
 
1.1%
8 17
 
0.6%
9 8
 
0.3%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 1
 
< 0.1%
9 8
 
0.3%
8 17
 
0.6%
7 29
 
1.1%
6 54
 
2.1%
5 88
 
3.4%
4 121
4.6%
3 182
7.0%
2 233
8.9%

Rzuty wolne H
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)2.7%
Missing1569
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean15.672069
Minimum0
Maximum32
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:14.349667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q112
median15
Q319
95-th percentile24
Maximum32
Range32
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6383721
Coefficient of variation (CV)0.29596425
Kurtosis0.34477177
Mean15.672069
Median Absolute Deviation (MAD)3
Skewness0.21157482
Sum16440
Variance21.514496
MonotonicityNot monotonic
2023-05-09T14:06:14.448243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
15 93
 
3.6%
14 89
 
3.4%
17 88
 
3.4%
13 85
 
3.2%
12 80
 
3.1%
16 74
 
2.8%
18 72
 
2.8%
19 69
 
2.6%
11 66
 
2.5%
20 55
 
2.1%
Other values (18) 278
 
10.6%
(Missing) 1569
59.9%
ValueCountFrequency (%)
0 5
 
0.2%
5 2
 
0.1%
6 7
 
0.3%
7 12
 
0.5%
8 19
 
0.7%
9 30
 
1.1%
10 50
1.9%
11 66
2.5%
12 80
3.1%
13 85
3.2%
ValueCountFrequency (%)
32 1
 
< 0.1%
30 3
 
0.1%
29 3
 
0.1%
28 4
 
0.2%
27 7
 
0.3%
26 6
 
0.2%
25 9
 
0.3%
24 24
0.9%
23 20
0.8%
22 40
1.5%

Rzuty wolne A
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)3.1%
Missing1569
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean15.993327
Minimum0
Maximum38
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:14.552273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q113
median16
Q319
95-th percentile24
Maximum38
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7185028
Coefficient of variation (CV)0.29502947
Kurtosis0.71974612
Mean15.993327
Median Absolute Deviation (MAD)3
Skewness0.2108545
Sum16777
Variance22.264268
MonotonicityNot monotonic
2023-05-09T14:06:14.655776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
14 104
 
4.0%
17 99
 
3.8%
18 90
 
3.4%
16 89
 
3.4%
13 75
 
2.9%
15 74
 
2.8%
19 73
 
2.8%
12 62
 
2.4%
20 49
 
1.9%
21 48
 
1.8%
Other values (22) 286
 
10.9%
(Missing) 1569
59.9%
ValueCountFrequency (%)
0 3
 
0.1%
3 1
 
< 0.1%
4 4
 
0.2%
5 5
 
0.2%
6 5
 
0.2%
7 11
 
0.4%
8 20
0.8%
9 28
1.1%
10 45
1.7%
11 47
1.8%
ValueCountFrequency (%)
38 1
 
< 0.1%
32 2
 
0.1%
31 1
 
< 0.1%
30 2
 
0.1%
29 2
 
0.1%
28 5
 
0.2%
27 4
 
0.2%
26 12
0.5%
25 13
0.5%
24 25
1.0%

Rzuty rożne H
Real number (ℝ)

Distinct19
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5156608
Minimum0
Maximum19
Zeros33
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:14.760417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q37
95-th percentile11
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8342208
Coefficient of variation (CV)0.51384973
Kurtosis0.53372624
Mean5.5156608
Median Absolute Deviation (MAD)2
Skewness0.65377568
Sum14440
Variance8.0328078
MonotonicityNot monotonic
2023-05-09T14:06:14.849087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 393
15.0%
5 379
14.5%
3 311
11.9%
6 308
11.8%
7 277
10.6%
2 222
8.5%
8 220
8.4%
9 159
6.1%
1 96
 
3.7%
10 75
 
2.9%
Other values (9) 178
6.8%
ValueCountFrequency (%)
0 33
 
1.3%
1 96
 
3.7%
2 222
8.5%
3 311
11.9%
4 393
15.0%
5 379
14.5%
6 308
11.8%
7 277
10.6%
8 220
8.4%
9 159
6.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
0.1%
15 9
 
0.3%
14 14
 
0.5%
13 18
 
0.7%
12 33
 
1.3%
11 66
2.5%
10 75
2.9%
9 159
6.1%

Rzuty rożne A
Real number (ℝ)

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7826585
Minimum0
Maximum16
Zeros49
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:14.948783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6069286
Coefficient of variation (CV)0.54507939
Kurtosis0.29781067
Mean4.7826585
Median Absolute Deviation (MAD)2
Skewness0.6319136
Sum12521
Variance6.7960767
MonotonicityNot monotonic
2023-05-09T14:06:15.039202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 407
15.5%
3 399
15.2%
5 389
14.9%
6 295
11.3%
2 294
11.2%
7 210
8.0%
1 169
6.5%
8 162
 
6.2%
9 115
 
4.4%
10 57
 
2.2%
Other values (7) 121
 
4.6%
ValueCountFrequency (%)
0 49
 
1.9%
1 169
6.5%
2 294
11.2%
3 399
15.2%
4 407
15.5%
5 389
14.9%
6 295
11.3%
7 210
8.0%
8 162
 
6.2%
9 115
 
4.4%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 3
 
0.1%
14 3
 
0.1%
13 7
 
0.3%
12 21
 
0.8%
11 37
 
1.4%
10 57
 
2.2%
9 115
4.4%
8 162
6.2%
7 210
8.0%

Spalone H
Real number (ℝ)

Distinct13
Distinct (%)0.5%
Missing16
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.8128363
Minimum0
Maximum15
Zeros519
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:15.134217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5735036
Coefficient of variation (CV)0.86797888
Kurtosis3.9000501
Mean1.8128363
Median Absolute Deviation (MAD)1
Skewness1.3731863
Sum4717
Variance2.4759136
MonotonicityNot monotonic
2023-05-09T14:06:15.221589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 778
29.7%
2 609
23.3%
0 519
19.8%
3 341
13.0%
4 199
 
7.6%
5 94
 
3.6%
6 36
 
1.4%
7 11
 
0.4%
8 7
 
0.3%
9 4
 
0.2%
Other values (3) 4
 
0.2%
(Missing) 16
 
0.6%
ValueCountFrequency (%)
0 519
19.8%
1 778
29.7%
2 609
23.3%
3 341
13.0%
4 199
 
7.6%
5 94
 
3.6%
6 36
 
1.4%
7 11
 
0.4%
8 7
 
0.3%
9 4
 
0.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 2
 
0.1%
10 1
 
< 0.1%
9 4
 
0.2%
8 7
 
0.3%
7 11
 
0.4%
6 36
 
1.4%
5 94
 
3.6%
4 199
7.6%
3 341
13.0%

Spalone A
Real number (ℝ)

Distinct12
Distinct (%)0.5%
Missing16
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.7409685
Minimum0
Maximum12
Zeros574
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:15.310434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5465869
Coefficient of variation (CV)0.8883486
Kurtosis2.6193677
Mean1.7409685
Median Absolute Deviation (MAD)1
Skewness1.27867
Sum4530
Variance2.3919311
MonotonicityNot monotonic
2023-05-09T14:06:15.402740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 759
29.0%
2 607
23.2%
0 574
21.9%
3 356
13.6%
4 163
 
6.2%
5 76
 
2.9%
6 35
 
1.3%
7 19
 
0.7%
8 8
 
0.3%
9 2
 
0.1%
Other values (2) 3
 
0.1%
(Missing) 16
 
0.6%
ValueCountFrequency (%)
0 574
21.9%
1 759
29.0%
2 607
23.2%
3 356
13.6%
4 163
 
6.2%
5 76
 
2.9%
6 35
 
1.3%
7 19
 
0.7%
8 8
 
0.3%
9 2
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 2
 
0.1%
9 2
 
0.1%
8 8
 
0.3%
7 19
 
0.7%
6 35
 
1.3%
5 76
 
2.9%
4 163
 
6.2%
3 356
13.6%
2 607
23.2%

Wrzuty z autu H
Real number (ℝ)

Distinct43
Distinct (%)4.1%
Missing1570
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean25.187023
Minimum0
Maximum53
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:15.509696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q121
median26
Q330
95-th percentile37
Maximum53
Range53
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.3564806
Coefficient of variation (CV)0.29207424
Kurtosis0.51784866
Mean25.187023
Median Absolute Deviation (MAD)5
Skewness-0.1700486
Sum26396
Variance54.117807
MonotonicityNot monotonic
2023-05-09T14:06:15.634560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
28 69
 
2.6%
26 65
 
2.5%
25 60
 
2.3%
23 59
 
2.3%
29 59
 
2.3%
27 59
 
2.3%
24 51
 
1.9%
22 50
 
1.9%
21 47
 
1.8%
30 42
 
1.6%
Other values (33) 487
 
18.6%
(Missing) 1570
60.0%
ValueCountFrequency (%)
0 5
 
0.2%
5 3
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 5
 
0.2%
9 6
 
0.2%
10 6
 
0.2%
11 7
0.3%
12 7
0.3%
13 16
0.6%
ValueCountFrequency (%)
53 1
 
< 0.1%
50 1
 
< 0.1%
44 6
 
0.2%
43 2
 
0.1%
42 4
 
0.2%
41 2
 
0.1%
40 7
 
0.3%
39 9
0.3%
38 10
0.4%
37 18
0.7%

Wrzuty z autu A
Real number (ℝ)

Distinct45
Distinct (%)4.3%
Missing1570
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean24.127863
Minimum0
Maximum53
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:15.750094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q120
median24
Q329
95-th percentile35
Maximum53
Range53
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8991138
Coefficient of variation (CV)0.2859397
Kurtosis0.4555936
Mean24.127863
Median Absolute Deviation (MAD)5
Skewness-0.12354646
Sum25286
Variance47.597771
MonotonicityNot monotonic
2023-05-09T14:06:15.877516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
24 76
 
2.9%
25 62
 
2.4%
23 62
 
2.4%
27 62
 
2.4%
28 57
 
2.2%
26 53
 
2.0%
29 53
 
2.0%
20 49
 
1.9%
22 48
 
1.8%
30 47
 
1.8%
Other values (35) 479
 
18.3%
(Missing) 1570
60.0%
ValueCountFrequency (%)
0 3
 
0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
6 3
 
0.1%
7 3
 
0.1%
8 4
 
0.2%
9 5
 
0.2%
10 7
0.3%
11 7
0.3%
12 15
0.6%
ValueCountFrequency (%)
53 1
 
< 0.1%
49 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 9
0.3%
38 4
0.2%

Interwencje bramkarzy H
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.048911
Minimum0
Maximum12
Zeros172
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:15.982152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9303368
Coefficient of variation (CV)0.63312337
Kurtosis0.49051486
Mean3.048911
Median Absolute Deviation (MAD)1
Skewness0.70712433
Sum7979
Variance3.7262001
MonotonicityNot monotonic
2023-05-09T14:06:16.080604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 547
20.9%
3 539
20.6%
1 414
15.8%
4 392
15.0%
5 273
10.4%
0 172
 
6.6%
6 142
 
5.4%
7 74
 
2.8%
8 35
 
1.3%
9 22
 
0.8%
Other values (3) 7
 
0.3%
ValueCountFrequency (%)
0 172
 
6.6%
1 414
15.8%
2 547
20.9%
3 539
20.6%
4 392
15.0%
5 273
10.4%
6 142
 
5.4%
7 74
 
2.8%
8 35
 
1.3%
9 22
 
0.8%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
10 5
 
0.2%
9 22
 
0.8%
8 35
 
1.3%
7 74
 
2.8%
6 142
 
5.4%
5 273
10.4%
4 392
15.0%
3 539
20.6%

Interwencje bramkarzy A
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.4631257
Minimum0
Maximum13
Zeros108
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:16.174480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0769497
Coefficient of variation (CV)0.59973269
Kurtosis0.77306455
Mean3.4631257
Median Absolute Deviation (MAD)1
Skewness0.74079764
Sum9063
Variance4.31372
MonotonicityNot monotonic
2023-05-09T14:06:16.270074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 492
18.8%
3 487
18.6%
4 456
17.4%
1 349
13.3%
5 313
12.0%
6 195
 
7.4%
7 116
 
4.4%
0 108
 
4.1%
8 54
 
2.1%
9 25
 
1.0%
Other values (4) 22
 
0.8%
ValueCountFrequency (%)
0 108
 
4.1%
1 349
13.3%
2 492
18.8%
3 487
18.6%
4 456
17.4%
5 313
12.0%
6 195
 
7.4%
7 116
 
4.4%
8 54
 
2.1%
9 25
 
1.0%
ValueCountFrequency (%)
13 3
 
0.1%
12 3
 
0.1%
11 6
 
0.2%
10 10
 
0.4%
9 25
 
1.0%
8 54
 
2.1%
7 116
 
4.4%
6 195
7.4%
5 313
12.0%
4 456
17.4%

Faule H
Real number (ℝ)

Distinct30
Distinct (%)1.1%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean14.459211
Minimum0
Maximum31
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:16.372158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median14
Q317
95-th percentile22
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2293631
Coefficient of variation (CV)0.29250303
Kurtosis0.11513941
Mean14.459211
Median Absolute Deviation (MAD)3
Skewness0.28872123
Sum37753
Variance17.887512
MonotonicityNot monotonic
2023-05-09T14:06:16.637964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
15 252
9.6%
14 246
9.4%
13 236
 
9.0%
12 229
 
8.7%
16 206
 
7.9%
11 201
 
7.7%
17 181
 
6.9%
18 172
 
6.6%
10 146
 
5.6%
19 132
 
5.0%
Other values (20) 610
23.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
4 9
 
0.3%
5 15
 
0.6%
6 18
 
0.7%
7 53
 
2.0%
8 95
3.6%
9 113
4.3%
10 146
5.6%
11 201
7.7%
ValueCountFrequency (%)
31 1
 
< 0.1%
30 1
 
< 0.1%
29 2
 
0.1%
28 2
 
0.1%
27 8
 
0.3%
26 11
 
0.4%
25 13
 
0.5%
24 25
1.0%
23 33
1.3%
22 44
1.7%

Faule A
Real number (ℝ)

Distinct33
Distinct (%)1.3%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean14.45653
Minimum0
Maximum32
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:16.746493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median14
Q317
95-th percentile22
Maximum32
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2097231
Coefficient of variation (CV)0.29119872
Kurtosis0.15427151
Mean14.45653
Median Absolute Deviation (MAD)3
Skewness0.29518318
Sum37746
Variance17.721769
MonotonicityNot monotonic
2023-05-09T14:06:16.848505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
13 257
9.8%
15 256
9.8%
14 230
 
8.8%
12 222
 
8.5%
17 203
 
7.8%
16 202
 
7.7%
11 180
 
6.9%
10 177
 
6.8%
18 152
 
5.8%
19 119
 
4.5%
Other values (23) 613
23.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 3
 
0.1%
3 1
 
< 0.1%
4 3
 
0.1%
5 13
 
0.5%
6 23
 
0.9%
7 39
 
1.5%
8 92
3.5%
9 119
4.5%
ValueCountFrequency (%)
32 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 2
 
0.1%
28 1
 
< 0.1%
27 6
 
0.2%
26 8
 
0.3%
25 14
0.5%
24 24
0.9%
23 32
1.2%

Czerwone kartki H
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.7%
Missing2169
Missing (%)82.8%
Memory size20.6 KiB
0.0
245 
1.0
197 
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1347
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 245
 
9.4%
1.0 197
 
7.5%
2.0 7
 
0.3%
(Missing) 2169
82.8%

Length

2023-05-09T14:06:16.952381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T14:06:17.061237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 245
54.6%
1.0 197
43.9%
2.0 7
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 694
51.5%
. 449
33.3%
1 197
 
14.6%
2 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 898
66.7%
Other Punctuation 449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 694
77.3%
1 197
 
21.9%
2 7
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 694
51.5%
. 449
33.3%
1 197
 
14.6%
2 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 694
51.5%
. 449
33.3%
1 197
 
14.6%
2 7
 
0.5%

Czerwone kartki A
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.9%
Missing2169
Missing (%)82.8%
Memory size20.6 KiB
1.0
259 
0.0
178 
2.0
 
11
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1347
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 259
 
9.9%
0.0 178
 
6.8%
2.0 11
 
0.4%
3.0 1
 
< 0.1%
(Missing) 2169
82.8%

Length

2023-05-09T14:06:17.149305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T14:06:17.256687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 259
57.7%
0.0 178
39.6%
2.0 11
 
2.4%
3.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 627
46.5%
. 449
33.3%
1 259
19.2%
2 11
 
0.8%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 898
66.7%
Other Punctuation 449
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 627
69.8%
1 259
28.8%
2 11
 
1.2%
3 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 627
46.5%
. 449
33.3%
1 259
19.2%
2 11
 
0.8%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 627
46.5%
. 449
33.3%
1 259
19.2%
2 11
 
0.8%
3 1
 
0.1%

Żółte kartki H
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.4%
Missing65
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2.0235018
Minimum0
Maximum8
Zeros272
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:17.346853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3273984
Coefficient of variation (CV)0.65599075
Kurtosis0.41495822
Mean2.0235018
Median Absolute Deviation (MAD)1
Skewness0.63897982
Sum5166
Variance1.7619866
MonotonicityNot monotonic
2023-05-09T14:06:17.437667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 726
27.7%
1 718
27.4%
3 499
19.1%
0 272
 
10.4%
4 233
 
8.9%
5 73
 
2.8%
6 23
 
0.9%
7 8
 
0.3%
8 1
 
< 0.1%
(Missing) 65
 
2.5%
ValueCountFrequency (%)
0 272
 
10.4%
1 718
27.4%
2 726
27.7%
3 499
19.1%
4 233
 
8.9%
5 73
 
2.8%
6 23
 
0.9%
7 8
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 8
 
0.3%
6 23
 
0.9%
5 73
 
2.8%
4 233
 
8.9%
3 499
19.1%
2 726
27.7%
1 718
27.4%
0 272
 
10.4%

Żółte kartki A
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.4%
Missing65
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2.2361927
Minimum0
Maximum8
Zeros201
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2023-05-09T14:06:17.532698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3612091
Coefficient of variation (CV)0.60871726
Kurtosis0.29555062
Mean2.2361927
Median Absolute Deviation (MAD)1
Skewness0.57323691
Sum5709
Variance1.8528902
MonotonicityNot monotonic
2023-05-09T14:06:17.620951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 768
29.3%
1 613
23.4%
3 522
19.9%
4 309
11.8%
0 201
 
7.7%
5 95
 
3.6%
6 34
 
1.3%
7 9
 
0.3%
8 2
 
0.1%
(Missing) 65
 
2.5%
ValueCountFrequency (%)
0 201
 
7.7%
1 613
23.4%
2 768
29.3%
3 522
19.9%
4 309
11.8%
5 95
 
3.6%
6 34
 
1.3%
7 9
 
0.3%
8 2
 
0.1%
ValueCountFrequency (%)
8 2
 
0.1%
7 9
 
0.3%
6 34
 
1.3%
5 95
 
3.6%
4 309
11.8%
3 522
19.9%
2 768
29.3%
1 613
23.4%
0 201
 
7.7%

Sezon
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.6 KiB
2021/2022
306 
2013/2014
296 
2014/2015
296 
2015/2016
296 
2016/2017
296 
Other values (4)
1128 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters23562
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013/2014
2nd row2013/2014
3rd row2013/2014
4th row2013/2014
5th row2013/2014

Common Values

ValueCountFrequency (%)
2021/2022 306
11.7%
2013/2014 296
11.3%
2014/2015 296
11.3%
2015/2016 296
11.3%
2016/2017 296
11.3%
2017/2018 296
11.3%
2018/2019 296
11.3%
2019/2020 296
11.3%
2020/2021 240
9.2%

Length

2023-05-09T14:06:17.725442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T14:06:17.844130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2021/2022 306
11.7%
2013/2014 296
11.3%
2014/2015 296
11.3%
2015/2016 296
11.3%
2016/2017 296
11.3%
2017/2018 296
11.3%
2018/2019 296
11.3%
2019/2020 296
11.3%
2020/2021 240
9.2%

Most occurring characters

ValueCountFrequency (%)
2 6930
29.4%
0 5772
24.5%
1 4394
18.6%
/ 2618
 
11.1%
4 592
 
2.5%
5 592
 
2.5%
6 592
 
2.5%
7 592
 
2.5%
8 592
 
2.5%
9 592
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20944
88.9%
Other Punctuation 2618
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6930
33.1%
0 5772
27.6%
1 4394
21.0%
4 592
 
2.8%
5 592
 
2.8%
6 592
 
2.8%
7 592
 
2.8%
8 592
 
2.8%
9 592
 
2.8%
3 296
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/ 2618
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23562
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6930
29.4%
0 5772
24.5%
1 4394
18.6%
/ 2618
 
11.1%
4 592
 
2.5%
5 592
 
2.5%
6 592
 
2.5%
7 592
 
2.5%
8 592
 
2.5%
9 592
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6930
29.4%
0 5772
24.5%
1 4394
18.6%
/ 2618
 
11.1%
4 592
 
2.5%
5 592
 
2.5%
6 592
 
2.5%
7 592
 
2.5%
8 592
 
2.5%
9 592
 
2.5%

Sędzia
Categorical

HIGH CARDINALITY  MISSING 

Distinct55
Distinct (%)2.1%
Missing46
Missing (%)1.8%
Memory size20.6 KiB
Marciniak S. (Pol)
230 
Frankowski B. (Pol)
219 
Kwiatkowski T. (Pol)
218 
Raczkowski P. (Pol)
216 
Przybyl J. (Pol)
204 
Other values (50)
1485 

Length

Max length47
Median length40
Mean length17.218507
Min length12

Characters and Unicode

Total characters44286
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowGil P. (Pol)
2nd rowTojo M. (Jpn)
3rd rowBorski M. (Pol)
4th rowZlotek M. (Pol)
5th rowRynkiewicz J. (Pol)

Common Values

ValueCountFrequency (%)
Marciniak S. (Pol) 230
 
8.8%
Frankowski B. (Pol) 219
 
8.4%
Kwiatkowski T. (Pol) 218
 
8.3%
Raczkowski P. (Pol) 216
 
8.3%
Przybyl J. (Pol) 204
 
7.8%
Jakubik K. (Pol) 199
 
7.6%
Musial T. (Pol) 184
 
7.0%
Stefanski D. (Pol) 182
 
7.0%
Gil P. (Pol) 175
 
6.7%
Zlotek M. (Pol) 140
 
5.3%
Other values (45) 605
23.1%

Length

2023-05-09T14:06:17.988089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pol 2469
31.6%
p 518
 
6.6%
t 430
 
5.5%
s 275
 
3.5%
d 263
 
3.4%
marciniak 230
 
2.9%
frankowski 221
 
2.8%
b 219
 
2.8%
kwiatkowski 218
 
2.8%
raczkowski 216
 
2.8%
Other values (88) 2759
35.3%

Most occurring characters

ValueCountFrequency (%)
5246
 
11.8%
o 3560
 
8.0%
l 3295
 
7.4%
P 3234
 
7.3%
k 2649
 
6.0%
( 2572
 
5.8%
) 2572
 
5.8%
i 2508
 
5.7%
. 2494
 
5.6%
a 2259
 
5.1%
Other values (47) 13897
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23591
53.3%
Uppercase Letter 7808
 
17.6%
Space Separator 5246
 
11.8%
Open Punctuation 2572
 
5.8%
Close Punctuation 2572
 
5.8%
Other Punctuation 2494
 
5.6%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3560
15.1%
l 3295
14.0%
k 2649
11.2%
i 2508
10.6%
a 2259
9.6%
s 1402
 
5.9%
w 1015
 
4.3%
r 955
 
4.0%
n 921
 
3.9%
z 725
 
3.1%
Other values (19) 4302
18.2%
Uppercase Letter
ValueCountFrequency (%)
P 3234
41.4%
M 681
 
8.7%
S 652
 
8.4%
K 510
 
6.5%
T 446
 
5.7%
J 438
 
5.6%
D 309
 
4.0%
B 290
 
3.7%
R 238
 
3.0%
F 221
 
2.8%
Other values (13) 789
 
10.1%
Space Separator
ValueCountFrequency (%)
5246
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2572
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2572
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2494
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31399
70.9%
Common 12887
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3560
 
11.3%
l 3295
 
10.5%
P 3234
 
10.3%
k 2649
 
8.4%
i 2508
 
8.0%
a 2259
 
7.2%
s 1402
 
4.5%
w 1015
 
3.2%
r 955
 
3.0%
n 921
 
2.9%
Other values (42) 9601
30.6%
Common
ValueCountFrequency (%)
5246
40.7%
( 2572
20.0%
) 2572
20.0%
. 2494
19.4%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44121
99.6%
None 165
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5246
 
11.9%
o 3560
 
8.1%
l 3295
 
7.5%
P 3234
 
7.3%
k 2649
 
6.0%
( 2572
 
5.8%
) 2572
 
5.8%
i 2508
 
5.7%
. 2494
 
5.7%
a 2259
 
5.1%
Other values (41) 13732
31.1%
None
ValueCountFrequency (%)
Ł 58
35.2%
ó 34
20.6%
ł 30
18.2%
ń 22
 
13.3%
ź 12
 
7.3%
ę 9
 
5.5%

Stadion
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct65
Distinct (%)2.7%
Missing188
Missing (%)7.2%
Memory size20.6 KiB
Stadion Miejski (Poznań)
 
158
Stadion Wojska Polskiego (Warszawa)
 
157
Stadion MKS Cracovia (Kraków)
 
155
Stadion Miejski w Szczecinie (Szczecin)
 
153
Stadion Miejski im. Henryka Reymana (Kraków)
 
152
Other values (60)
1655 

Length

Max length47
Median length40
Mean length30.428395
Min length5

Characters and Unicode

Total characters73941
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)1.4%

Sample

1st rowStadion im. Ernesta Pohla (Zabrze)
2nd rowStadion Wojska Polskiego (Warszawa)
3rd rowStadion Ruchu Chorzów (Chorzów)
4th rowStadion Miejski im. Henryka Reymana (Kraków)
5th rowStadion MKS Cracovia (Kraków)

Common Values

ValueCountFrequency (%)
Stadion Miejski (Poznań) 158
 
6.0%
Stadion Wojska Polskiego (Warszawa) 157
 
6.0%
Stadion MKS Cracovia (Kraków) 155
 
5.9%
Stadion Miejski w Szczecinie (Szczecin) 153
 
5.8%
Stadion Miejski im. Henryka Reymana (Kraków) 152
 
5.8%
Tarczyński Arena (Wrocław) 151
 
5.8%
Stadion Miejski w Gliwicach (Gliwice) 151
 
5.8%
Polsat Plus Arena (Gdańsk) 149
 
5.7%
Stadion im. Ernesta Pohla (Zabrze) 137
 
5.2%
Stadion Zagłębia Lubin (Lubin) 132
 
5.0%
Other values (55) 935
35.7%
(Missing) 188
 
7.2%

Length

2023-05-09T14:06:18.096979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadion 1862
 
19.4%
miejski 821
 
8.6%
arena 472
 
4.9%
im 336
 
3.5%
kraków 307
 
3.2%
w 304
 
3.2%
lubin 264
 
2.8%
poznań 158
 
1.6%
polskiego 157
 
1.6%
warszawa 157
 
1.6%
Other values (118) 4743
49.5%

Most occurring characters

ValueCountFrequency (%)
i 7242
 
9.8%
7151
 
9.7%
a 6592
 
8.9%
o 3852
 
5.2%
n 3852
 
5.2%
e 3675
 
5.0%
k 2671
 
3.6%
S 2669
 
3.6%
s 2458
 
3.3%
t 2407
 
3.3%
Other values (56) 31372
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51433
69.6%
Uppercase Letter 9946
 
13.5%
Space Separator 7151
 
9.7%
Open Punctuation 2398
 
3.2%
Close Punctuation 2398
 
3.2%
Other Punctuation 336
 
0.5%
Dash Punctuation 140
 
0.2%
Decimal Number 139
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7242
14.1%
a 6592
12.8%
o 3852
 
7.5%
n 3852
 
7.5%
e 3675
 
7.1%
k 2671
 
5.2%
s 2458
 
4.8%
t 2407
 
4.7%
c 2373
 
4.6%
r 2336
 
4.5%
Other values (19) 13975
27.2%
Uppercase Letter
ValueCountFrequency (%)
S 2669
26.8%
M 1056
 
10.6%
P 872
 
8.8%
G 723
 
7.3%
K 693
 
7.0%
W 510
 
5.1%
A 508
 
5.1%
B 483
 
4.9%
R 372
 
3.7%
L 338
 
3.4%
Other values (12) 1722
17.3%
Decimal Number
ValueCountFrequency (%)
0 20
14.4%
1 17
12.2%
2 17
12.2%
9 15
10.8%
5 14
10.1%
3 13
9.4%
7 12
8.6%
4 11
7.9%
8 11
7.9%
6 9
6.5%
Space Separator
ValueCountFrequency (%)
7151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2398
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2398
100.0%
Other Punctuation
ValueCountFrequency (%)
. 336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61379
83.0%
Common 12562
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7242
 
11.8%
a 6592
 
10.7%
o 3852
 
6.3%
n 3852
 
6.3%
e 3675
 
6.0%
k 2671
 
4.4%
S 2669
 
4.3%
s 2458
 
4.0%
t 2407
 
3.9%
c 2373
 
3.9%
Other values (41) 23588
38.4%
Common
ValueCountFrequency (%)
7151
56.9%
( 2398
 
19.1%
) 2398
 
19.1%
. 336
 
2.7%
- 140
 
1.1%
0 20
 
0.2%
1 17
 
0.1%
2 17
 
0.1%
9 15
 
0.1%
5 14
 
0.1%
Other values (5) 56
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71857
97.2%
None 2084
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7242
 
10.1%
7151
 
10.0%
a 6592
 
9.2%
o 3852
 
5.4%
n 3852
 
5.4%
e 3675
 
5.1%
k 2671
 
3.7%
S 2669
 
3.7%
s 2458
 
3.4%
t 2407
 
3.3%
Other values (49) 29288
40.8%
None
ValueCountFrequency (%)
ł 698
33.5%
ó 555
26.6%
ń 458
22.0%
ę 215
 
10.3%
Ł 118
 
5.7%
ź 36
 
1.7%
ą 4
 
0.2%

Interactions

2023-05-09T14:06:08.610542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-09T14:05:36.009619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-05-09T14:05:40.659991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:42.933094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:45.384189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:47.687670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:50.047946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:52.322334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:54.699281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:56.897911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:05:59.330490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:06:01.590402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:06:03.828961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:06:06.182177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-09T14:06:08.499220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-09T14:06:18.218442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Sytuacje bramkowe HSytuacje bramkowe AStrzały na bramkę HStrzały na bramkę AStrzały niecelne HStrzały niecelne AStrzały zablokowane HStrzały zablokowane ARzuty wolne HRzuty wolne ARzuty rożne HRzuty rożne ASpalone HSpalone AWrzuty z autu HWrzuty z autu AInterwencje bramkarzy HInterwencje bramkarzy AFaule HFaule AŻółte kartki HŻółte kartki AGospodarzeGościePosiadanie piłki HPosiadanie piłki ACzerwone kartki HCzerwone kartki ASezonSędziaStadion
Sytuacje bramkowe H1.000-0.1850.613-0.1450.715-0.1480.563-0.171-0.034-0.0970.405-0.201-0.004-0.0210.083-0.112-0.1530.589-0.091-0.011-0.0600.0270.0830.0930.2170.2170.2220.2010.0510.0000.027
Sytuacje bramkowe A-0.1851.000-0.1150.622-0.1620.716-0.1870.609-0.074-0.009-0.1900.392-0.016-0.020-0.1370.0010.589-0.148-0.031-0.0830.063-0.0480.0770.0480.1340.1340.2630.2080.0460.0440.127
Strzały na bramkę H0.613-0.1151.000-0.0910.123-0.0810.053-0.082-0.0610.0420.205-0.0900.053-0.024-0.048-0.028-0.0690.849-0.019-0.015-0.0500.0270.0520.0840.0760.0760.2380.2090.0000.0000.000
Strzały na bramkę A-0.1450.622-0.0911.000-0.0930.126-0.1260.165-0.025-0.045-0.1280.261-0.0390.031-0.063-0.0090.863-0.101-0.062-0.0490.061-0.0390.0800.0330.0120.0120.3320.1800.0000.0000.094
Strzały niecelne H0.715-0.1620.123-0.0931.0000.0190.195-0.1470.019-0.1230.305-0.163-0.051-0.0060.078-0.120-0.1120.176-0.0620.025-0.0160.0160.0830.0500.1640.1640.1410.2120.1210.0000.040
Strzały niecelne A-0.1480.716-0.0810.1260.0191.000-0.1400.195-0.0800.008-0.1350.268-0.014-0.049-0.092-0.0130.174-0.1070.040-0.0340.052-0.0270.0790.0420.1220.1220.1660.1340.1070.0000.064
Strzały zablokowane H0.563-0.1870.053-0.1260.195-0.1401.000-0.108-0.014-0.0990.343-0.154-0.061-0.0370.129-0.074-0.1500.119-0.0570.015-0.022-0.0070.1590.1540.1870.1870.0000.0000.0740.0000.137
Strzały zablokowane A-0.1710.609-0.0820.165-0.1470.195-0.1081.000-0.008-0.008-0.1560.297-0.005-0.015-0.1180.0650.172-0.1140.0040.0010.0520.0180.1160.0610.0630.0630.0900.1710.0430.0510.166
Rzuty wolne H-0.034-0.074-0.061-0.0250.019-0.080-0.014-0.0081.0000.034-0.032-0.058-0.0520.3390.067-0.027-0.025-0.0060.0460.8420.0410.2110.1330.1360.1110.1110.0950.0000.2010.0220.216
Rzuty wolne A-0.097-0.0090.042-0.045-0.1230.008-0.099-0.0080.0341.000-0.031-0.0160.350-0.0660.0650.071-0.0040.0110.8690.0390.243-0.0490.0680.1030.0620.0620.0000.0000.1220.1150.000
Rzuty rożne H0.405-0.1900.205-0.1280.305-0.1350.343-0.156-0.032-0.0311.000-0.1830.020-0.0210.098-0.115-0.1260.272-0.045-0.018-0.047-0.0090.0380.0270.1860.1860.1400.0740.0000.2400.000
Rzuty rożne A-0.2010.392-0.0900.261-0.1630.268-0.1540.297-0.058-0.016-0.1831.000-0.0330.058-0.1370.0790.298-0.111-0.041-0.0700.004-0.0280.0460.0420.0790.0790.0890.0060.0130.0000.165
Spalone H-0.004-0.0160.053-0.039-0.051-0.014-0.061-0.005-0.0520.3500.020-0.0331.000-0.0330.015-0.025-0.0200.0440.011-0.0000.002-0.0030.0000.0860.0900.0900.0000.1230.0380.1430.000
Spalone A-0.021-0.020-0.0240.031-0.006-0.049-0.037-0.0150.339-0.066-0.0210.058-0.0331.000-0.043-0.0210.022-0.015-0.0340.0050.025-0.0300.0700.0080.0840.0840.1280.0000.0000.0810.000
Wrzuty z autu H0.083-0.137-0.048-0.0630.078-0.0920.129-0.1180.0670.0650.098-0.1370.015-0.0431.0000.272-0.0750.0230.0250.0390.012-0.0140.1700.0760.0000.0000.4740.0000.0870.0520.169
Wrzuty z autu A-0.1120.001-0.028-0.009-0.120-0.013-0.0740.065-0.0270.071-0.1150.079-0.025-0.0210.2721.0000.046-0.0720.020-0.083-0.0190.0120.1640.0000.0520.0520.2140.2540.1090.0000.152
Interwencje bramkarzy H-0.1530.589-0.0690.863-0.1120.174-0.1500.172-0.025-0.004-0.1260.298-0.0200.022-0.0750.0461.000-0.075-0.040-0.0500.033-0.0370.0380.0510.0340.0340.2110.1460.0220.0000.064
Interwencje bramkarzy A0.589-0.1480.849-0.1010.176-0.1070.119-0.114-0.0060.0110.272-0.1110.044-0.0150.023-0.072-0.0751.000-0.0250.005-0.0230.0160.0230.0430.1080.1080.1590.0990.0320.0000.000
Faule H-0.091-0.031-0.019-0.062-0.0620.040-0.0570.0040.0460.869-0.045-0.0410.011-0.0340.0250.020-0.040-0.0251.0000.0880.2600.0270.0540.0400.0000.0000.0000.0000.0300.0440.000
Faule A-0.011-0.083-0.015-0.0490.025-0.0340.0150.0010.8420.039-0.018-0.070-0.0000.0050.039-0.083-0.0500.0050.0881.0000.0320.2550.0530.0760.0460.0460.0000.0000.0450.0800.000
Żółte kartki H-0.0600.063-0.0500.061-0.0160.052-0.0220.0520.0410.243-0.0470.0040.0020.0250.012-0.0190.033-0.0230.2600.0321.0000.1250.0600.0290.0000.0000.2580.1720.0240.0000.000
Żółte kartki A0.027-0.0480.027-0.0390.016-0.027-0.0070.0180.211-0.049-0.009-0.028-0.003-0.030-0.0140.012-0.0370.0160.0270.2550.1251.0000.0320.0000.0000.0000.2170.2910.0310.0000.000
Gospodarze0.0830.0770.0520.0800.0830.0790.1590.1160.1330.0680.0380.0460.0000.0700.1700.1640.0380.0230.0540.0530.0600.0321.0000.0000.0580.0580.0000.0000.2940.2000.990
Goście0.0930.0480.0840.0330.0500.0420.1540.0610.1360.1030.0270.0420.0860.0080.0760.0000.0510.0430.0400.0760.0290.0000.0001.0000.0460.0460.0420.0000.2980.0690.000
Posiadanie piłki H0.2170.1340.0760.0120.1640.1220.1870.0630.1110.0620.1860.0790.0900.0840.0000.0520.0340.1080.0000.0460.0000.0000.0580.0461.0001.0000.2230.0830.0600.0930.144
Posiadanie piłki A0.2170.1340.0760.0120.1640.1220.1870.0630.1110.0620.1860.0790.0900.0840.0000.0520.0340.1080.0000.0460.0000.0000.0580.0461.0001.0000.2230.0830.0600.0930.144
Czerwone kartki H0.2220.2630.2380.3320.1410.1660.0000.0900.0950.0000.1400.0890.0000.1280.4740.2140.2110.1590.0000.0000.2580.2170.0000.0420.2230.2231.0000.6250.0330.0000.000
Czerwone kartki A0.2010.2080.2090.1800.2120.1340.0000.1710.0000.0000.0740.0060.1230.0000.0000.2540.1460.0990.0000.0000.1720.2910.0000.0000.0830.0830.6251.0000.0000.0000.000
Sezon0.0510.0460.0000.0000.1210.1070.0740.0430.2010.1220.0000.0130.0380.0000.0870.1090.0220.0320.0300.0450.0240.0310.2940.2980.0600.0600.0330.0001.0000.2630.303
Sędzia0.0000.0440.0000.0000.0000.0000.0000.0510.0220.1150.2400.0000.1430.0810.0520.0000.0000.0000.0440.0800.0000.0000.2000.0690.0930.0930.0000.0000.2631.0000.485
Stadion0.0270.1270.0000.0940.0400.0640.1370.1660.2160.0000.0000.1650.0000.0000.1690.1520.0640.0000.0000.0000.0000.0000.9900.0000.1440.1440.0000.0000.3030.4851.000

Missing values

2023-05-09T14:06:11.182045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-09T14:06:11.597216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-09T14:06:11.957420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GospodarzeGościePosiadanie piłki HPosiadanie piłki ASytuacje bramkowe HSytuacje bramkowe AStrzały na bramkę HStrzały na bramkę AStrzały niecelne HStrzały niecelne AStrzały zablokowane HStrzały zablokowane ARzuty wolne HRzuty wolne ARzuty rożne HRzuty rożne ASpalone HSpalone AWrzuty z autu HWrzuty z autu AInterwencje bramkarzy HInterwencje bramkarzy AFaule HFaule ACzerwone kartki HCzerwone kartki AŻółte kartki HŻółte kartki ASezonSędziaStadion
0Górnik ZabrzeLechia Gdańsk52%48%22.014.076867.02.022.017.0654.02.027.021.04.07.014.021.0NaNNaN3.01.02013/2014Gil P. (Pol)Stadion im. Ernesta Pohla (Zabrze)
1Legia WarszawaLech Poznań49%51%10.08.046620.00.020.015.01030.00.020.037.06.02.09.010.0NaNNaN2.04.02013/2014Tojo M. (Jpn)Stadion Wojska Polskiego (Warszawa)
2Ruch ChorzówPogoń Szczecin45%55%13.09.021883.00.016.010.0440.04.011.012.01.02.011.011.0NaNNaN0.03.02013/2014Borski M. (Pol)Stadion Ruchu Chorzów (Chorzów)
3Wisła KrakówZawisza Bydgoszcz49%51%11.019.065481.06.018.026.0543.00.013.016.04.04.026.018.0NaNNaN0.05.02013/2014Zlotek M. (Pol)Stadion Miejski im. Henryka Reymana (Kraków)
4CracoviaJagiellonia Białystok63%37%16.019.078871.04.011.020.0353.00.020.019.06.05.019.012.0NaNNaN1.00.02013/2014Rynkiewicz J. (Pol)Stadion MKS Cracovia (Kraków)
5Korona KielceŚląsk Wrocław46%54%10.025.06102102.05.07.014.0452.00.019.024.05.06.012.07.0NaNNaN2.01.02013/2014Krasny S. (Pol)Suzuki Arena (Kielce)
6Piast GliwicePodbeskidzie B-B45%55%6.023.039271.07.013.014.0262.01.028.023.07.01.012.013.0NaNNaN1.04.02013/2014Garbowski T. (Pol)Stadion Miejski w Gliwicach (Gliwice)
7Zagłębie LubinWidzew Łódź49%51%20.014.079954.00.014.018.0634.00.021.017.09.07.015.012.0NaNNaN2.01.02013/2014Jarzebak S. (Pol)Stadion Zagłębia Lubin (Lubin)
8Widzew ŁódźPiast Gliwice54%46%19.015.076666.03.0NaNNaN261.03.0NaNNaN5.05.014.021.0NaNNaN1.03.02013/2014Gil P. (Pol)Stadion Widzewa Łódź (Łódź)
9Lech PoznańRuch Chorzów55%45%14.010.062672.01.012.022.0408.01.034.029.02.02.014.011.0NaNNaN2.00.02013/2014Tojo M. (Jpn)Stadion Miejski (Poznań)
GospodarzeGościePosiadanie piłki HPosiadanie piłki ASytuacje bramkowe HSytuacje bramkowe AStrzały na bramkę HStrzały na bramkę AStrzały niecelne HStrzały niecelne AStrzały zablokowane HStrzały zablokowane ARzuty wolne HRzuty wolne ARzuty rożne HRzuty rożne ASpalone HSpalone AWrzuty z autu HWrzuty z autu AInterwencje bramkarzy HInterwencje bramkarzy AFaule HFaule ACzerwone kartki HCzerwone kartki AŻółte kartki HŻółte kartki ASezonSędziaStadion
2608Zagłębie LubinGórnik Łęczna51%49%7.04.04232NaNNaNNaNNaN241.03.0NaNNaN1.01.013.013.0NaNNaN0.02.02021/2022Krasny S. (Pol)Stadion Zagłębia Lubin (Lubin)
2609Wisła KrakówZagłębie Lubin57%43%9.012.05448NaNNaNNaNNaN852.00.0NaNNaN4.02.018.016.0NaNNaN4.01.02021/2022Lasyk P. (Pol)Stadion Miejski im. Henryka Reymana (Kraków)
2610Pogoń SzczecinGórnik Zabrze55%45%8.05.04144NaNNaNNaNNaN652.00.0NaNNaN1.02.011.013.0NaNNaN1.01.02021/2022Przybyl J. (Pol)Stadion Miejski w Szczecinie (Szczecin)
2611Śląsk WrocławWarta Poznań52%48%17.011.055126NaNNaNNaNNaN140.00.0NaNNaN3.03.010.014.0NaNNaN2.01.02021/2022Kuzma L. (Pol)Tarczyński Arena (Wrocław)
2612Piast GliwiceRaków Częstochowa54%46%12.016.07957NaNNaNNaNNaN461.01.0NaNNaN6.05.014.017.0NaNNaN2.02.02021/2022Marciniak S. (Pol)Stadion Miejski w Gliwicach (Gliwice)
2613Legia WarszawaWisła Płock55%45%9.016.024712NaNNaNNaNNaN062.03.0NaNNaN4.01.010.020.0NaNNaN2.02.02021/2022Kos D. (Pol)Stadion Wojska Polskiego (Warszawa)
2614Jagiellonia BiałystokLechia Gdańsk52%48%19.012.054148NaNNaNNaNNaN591.01.0NaNNaN3.04.012.011.0NaNNaN2.01.02021/2022Kwiatkowski T. (Pol)Stadion Miejski (Białystok)
2615Górnik ŁęcznaCracovia48%52%11.018.027911NaNNaNNaNNaN770.01.0NaNNaN6.01.013.010.0NaNNaN3.03.02021/2022Szczech Ł. (Pol)Stadion GKS Górnik Łęczna (Łęczna)
2616Lech PoznańRadomiak Radom55%45%10.07.06146NaNNaNNaNNaN956.02.0NaNNaN1.06.010.09.0NaNNaN1.01.02021/2022Frankowski B. (Pol)Stadion Miejski (Poznań)
2617Bruk-Bet T.Stal Mielec52%48%10.04.03371NaNNaNNaNNaN123.00.0NaNNaN2.02.011.016.0NaNNaN2.02.02021/2022Dobrynin Z. (Pol)Termalica Bruk-Bet Nieciecza (Nieciecza)